Created
November 18, 2016 00:02
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class MyOp(theano.Op): | |
# Properties attribute | |
#itypes and otypes attributes are | |
#compulsory if make_node method is not defined. | |
#They're the type of input and output respectively | |
itypes = [cuda.CudaNdarrayType([False,False,False,False])] | |
otypes = [cuda.CudaNdarrayType([False]*4)] | |
def __init__(self,r): | |
self.r = r | |
super(MyOp,self).__init__() | |
def perform(self, node, inputs, output_storage): | |
input = inputs[0] | |
input_shape=input.shape | |
out = T.zeros((input_shape[0]//self.r**2,input_shape[1],self.r*input_shape[2],self.r*input_shape[3])) | |
batch_size = input.shape[0]//self.r**2 | |
for x in xrange(self.r): # loop across all feature maps belonging to this channel | |
for y in xrange(self.r): | |
out=T.set_subtensor(out[:,:,x::self.r,y::self.r],input[batch_size*(self.r*x+y):batch_size*(self.r*x+y+1),:,:,:]) | |
return out | |
# Other type of implementation | |
def infer_shape(self,node, input_shapes): | |
input_shape = input_shapes[0] | |
return [(input_shape[0]//self.r**2,input_shape[1],self.r*input_shape[2],self.r*input_shape[3])] | |
def grad(self, inputs, output_grads): | |
out = output_grads[0] | |
return T.concatenate([out[:,:,x::self.r,y::self.r] for x in xrange(self.r) for y in xrange(self.r)],axis=0) |
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